Systems and methods of dynamic optimization of data element utilization according to objectives

ABSTRACT

Systems and methods are disclosed for optimizing data element usage according to user-defined objectives, comprising receiving a plurality of user-defined objectives associated with a group of data elements; receiving one or more constraints associated with the group of data elements, wherein at least one of the constraints comprises resources apportionable to each data element in the group of data elements; apportioning at least a portion of the resources to each data element in the group of data elements in a manner that meets the one or more constraints; receiving metrics associated with the performance of the group of data elements in meeting the plurality of user-defined objectives; determining an effectiveness of each data element in the group of data elements for meeting the plurality of user-defined objectives; and automatically revising the at least a portion of resources associated with each data element in the group of data elements.

TECHNICAL FIELD

The disclosure generally relates to the field of data elementoptimization. More specifically, the disclosure relates to optimizingdata element usage according to objectives.

BACKGROUND

Conventional data element optimization is a complex and time consumingprocess, and is often not quantified. For example, producers of onlinevideos and other data elements promoting products and/or services mayhave a limited budget. Based on limited data, the producers may chooseto use one promotional data element more often than another, but thisdecision is often based on subjective feelings about the merits of thedata element's content.

Further, producers of data elements often have a plurality of objectivesand constraints associated with the promotion of products and services.Prioritizing the objectives relative to each other while meeting allconstraints is difficult, if not impossible, in real time.

SUMMARY OF THE DISCLOSURE

Systems and methods disclosed may optimize data element usage accordingto user-defined objectives, comprising receiving a plurality ofuser-defined objectives associated with a group of data elements;receiving one or more constraints associated with the group of dataelements, wherein at least one of the constraints comprises resourcesapportionable to each data element in the group of data elements;apportioning at least a portion of the resources to each data element inthe group of data elements in a manner that meets the one or moreconstraints; receiving one or more metrics associated with theperformance of the group of data elements in meeting the plurality ofuser-defined objectives; determining an effectiveness of each dataelement in the group of data elements for meeting the plurality ofuser-defined objectives, wherein the effectiveness is determined basedon the one or more metrics; and automatically revising the at least aportion of resources associated with each data element in the group ofdata elements based on the determined effectiveness in a manneroptimized to meet the plurality of user-defined objectives within theone or more constraints.

Further techniques described herein may comprise receiving an additionalconstraint for association with the one or more constraints; andautomatically revising the at least a portion of resources associatedwith each data element in the group of data elements based on theadditional constraint. Automatically revising the at least a portion ofresources associated with each data element in the group of dataelements may further comprise receiving a weighting of each user-definedobjective of the plurality of user-defined objectives; and automaticallyrevising the at least a portion of resources associated with each dataelement in the group of data elements based on the determinedeffectiveness in a manner prioritizing a highest-weighted user-definedobjective of the plurality of user-defined objectives within theplurality of constraints.

Systems and methods presented herein may further comprise designating apredetermined minimum allocation of the resources to each data elementin the group of data elements until a predetermined minimum level ofmetrics associated with each data element in the group of data elementsis obtained. Systems and methods may further lower the predeterminedminimum allocation of the resources for each data element in the groupof data elements as metrics associated with each data element in thegroup of data elements are obtained.

Systems and methods described herein may further automatically revisethe at least a portion of resources associated with each data element inthe group of data elements by determining at least one feasibilityregion based on the one or more constraints associated with the group ofdata elements; determining intersection points of constraints to formone or more candidate optimization points; and selecting a candidateoptimization point from the one or more candidate optimization points bydetermining which candidate optimization point maximizes a highestpriority user-defined objective from the plurality of user-definedobjectives.

In system and methods described herein the one or more metrics may bedetermined over a predetermined time interval. Further, each dataelement in the group of data elements may correspond to a promotionalvideo. Further, the resources may comprise financial resources.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages of the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 is a high-level block diagram illustrating a system fordynamically optimizing the use of data elements in accordance withobjectives and constraints.

FIG. 2 is a high-level block diagram illustrating an example of acomputer for use as a server and/or as a client according to techniquespresented herein.

FIG. 3 is a block diagram illustrating an example object hierarchyaccording to techniques presented herein.

FIG. 4 is a block diagram illustrating an example of the grouping ofdata elements into data element groups according to techniques presentedherein.

FIG. 5 is an example user interface displaying data elements and dataelement groups.

FIG. 6 is an example user interface allowing the creation of one or moredata element groups according to techniques presented herein.

FIG. 7 is an example user interface enabling the creation of one or moredata element groups according to techniques presented herein.

FIG. 8 is an example user interface enabling the selection of objectivesassociated with groups of data elements according to techniquespresented herein.

FIG. 9 is an example user interface enabling the creation and/orselection of data elements that may be associated with a data elementgroup.

FIG. 10 is an example user interface enabling the creation and/orselection of data elements that may be associated with a data elementgroup.

FIG. 11 is an example user interface displaying data elements associatedwith a data element group according to techniques presented herein.

FIG. 12 is a flow diagram illustrating an example method according totechniques presented herein.

DETAILED DESCRIPTION

Reference will now be made in detail to the exemplary embodiments of thedisclosure, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

FIG. 1 is a high-level block diagram of a computing environment 100 fordynamically optimizing data elements according to one embodiment. Thecomputing environment 100 may include a publisher server 110, a dataelement server 120, and a client 130 communicatively coupled by anetwork 190. In one embodiment, the publisher server 110 and the dataelement server 120 may be web servers. In another embodiment, thepublisher server 110 may be an application server that provides aninstance of one or more applications 140 to the client 130. In yetanother embodiment, the publisher server 110 and data element server 120may provide data to support the execution of the application 140 on theclient 130. The client 130 is a computer or other electronic devicewhich may be used by one or more users to perform activities which mayinclude browsing web pages on the network 190, or using application 140.The client 130, for example, may be a personal computer, personaldigital assistant (PDA), or a mobile telephone. Only one publisherserver 110, one data element server 120, and one client 130 are shown inFIG. 1 in order to simplify and clarify the description. Otherembodiments of the computing environment 100 may include any number ofpublisher servers 110, data element servers 120, and/or clients 130connected to the network 190. Further, while the publisher server 110and data element server 120 are depicted as separate in the example ofFIG. 1, the features of both the publisher server 110 and data elementserver 120 may be integrated into a single device on the network 190.

The network 190 represents the communication pathways between (e.g.,communicatively coupled) the publisher server 110, data element server120, and client 130. In one embodiment, the network 190 is the Internet.The network 190 may also include dedicated or private communicationslinks that are not necessarily a part of the Internet. In oneembodiment, the network 190 uses various communications technologiesand/or protocols. Thus, the network 190 may include links usingtechnologies such as Ethernet, 802.11, integrated services digitalnetwork (ISDN), digital subscriber line (DSL), asynchronous transfermode (ATM), etc. Similarly, the networking protocols used on the network190 may include the transmission control protocol/Internet protocol(TCP/IP), the hypertext transport protocol (HTTP), the simple mailtransfer protocol (SMTP), the file transfer protocol (FTP), etc. Thedata exchanged over the network 190 can be represented usingtechnologies and/or formats including the hypertext markup language(HTML), the extensible markup language (XML), etc. In addition, all orsome of links may be encrypted using encryption technologies such as thesecure sockets layer (SSL), transport layer security (TLS), secure HTTP(HTTPS), and/or virtual private networks (VPNs). In another embodiment,the entities may use custom and/or dedicated data communicationstechnologies instead of, or in addition to, the ones described above.

As shown in FIG. 1, client 130 may execute an application, such as a webapplication or browser, that allows a user to retrieve and view contentstored on other computers or servers on the network 190. The application140 may also allow the user to submit information to other computers onthe network 190, such as through user interfaces 150, web pages,application program interfaces (APIs), and/or other data portals. In oneembodiment, the application 140 is a web browser, such as MICROSOFTINTERNET EXPLORER or MOZILLA FIREFOX. The application 140 may supporttechnologies including JavaScript, ActionScript, and other scriptinglanguages that allow the client 130 to perform actions in response toscripts and other data sent to the application via the network 190. Insome embodiments, functions ascribed herein to the application 140 areimplemented via plug-ins such as ADOBE FLASH.

The publisher server 110 may deliver data associated with a userinterface 150, such as a web page, to the application 140 over thenetwork 190. The application 140 may then load the user interface 150and present it to the user. User interface 150 may correspond to any ofthe user interfaces discussed herein, and any of the user interfaceswhich may be displayed by application 140. The user interface 150 mayinclude a video player 170 for presenting online videos and a dataelement player 160 which may present associated and/or promotionalmaterials to the user. The data element player 160 may be used todisplay any of the data elements discussed herein to a user. The videoplayer 170 can be any video player suitable for online video such asWINDOWS MEDIA PLAYER, REALPLAYER, QUICKTIME, WINAMP, or any number ofcustom video players built to run on a suitable platform such as theAdobe Flash platform.

The data element player 160 may comprise JavaScript, ActionScript and/orother code executable by the application 140 that may be delivered tothe client 130 in addition to or as part of the user interface 150. Adata element script 180 may contain code readable and/or transformableby the data element player 160 into operational instructions that governbehavior of the data element player 160. The application may execute thedata element player 160 natively, directly (e.g., as JavaScript) or viaa browser plug-in module (e.g., as a Flash plug-in). The data elementplayer 160 may communicate with the data element server 120 over thenetwork 190 to request and receive content for presentation on theclient 130. A data element may comprise any computer-executable code(e.g., JavaScript, ActionScript, Flash, or HTML) whose execution mayresult in the presentation of text, images, and/or sounds to the user.The text, images, and/or sounds may promote one or more products,services, viewpoints and/or actions. A data element can be a linear dataelement (i.e., promotional content that interrupts the presentation of avideo) or a non-linear data element (i.e., promotional content that ispresented concurrently with a video) presented either before, during, orafter the video. A data element can also be textual, graphical (such asa banner promotion), or a video promotion. A data element can bepresented as overlaying the online video or in any other position withinthe user interface 150. A data element can also be interactive and, inone embodiment, a data element can transition from one of theaforementioned varieties of promotional data elements to a differentvariety or trigger an additional data element in response to an actionby the user.

FIG. 2 is a high-level block diagram illustrating on example of acomputer 200 for use as a client 130 and/or as a server, such as apublisher server 110 or a data element server 120. Illustrated are atleast one processor 202 coupled to a chipset 204. The chipset 204 mayinclude a memory controller hub 220 and/or an input/output (I/O)controller hub 222. A memory 206 and a graphics adapter 212 may becoupled to the memory controller hub 220, and a display 218 is coupledto the graphics adapter 212. A storage device 208, keyboard 210,pointing device 214, and network adapter 216 may be coupled to the I/Ocontroller hub 222. Other embodiments of the computer 200 have differentarchitectures. For example, the memory 206 may be directly coupled tothe processor 202 in some embodiments.

The computer 200 may be adapted to execute computer program modules forproviding the functionality described herein. As used herein, the term“module” refers to computer program logic configured and used to providethe specified functionality. Thus, a module can be implemented inhardware, firmware, and/or software. In one embodiment, program modulesare stored on the storage device 208, loaded into the memory 206, andexecuted by the processor 202. The storage device 208 is acomputer-readable storage medium such as a hard drive, compact diskread-only memory (CD-ROM), DVD, or a solid-state memory device. Thememory 206 is also a computer-readable storage medium and storescomputer-executable instructions and data used by the processor 202.

In one embodiment, the memory 206 stores computer-executableinstructions that cause the processor 202 to implement a method fordisplaying data elements. The computer-executable instructions stored bythe memory comprise instructions for the application 140. In oneembodiment, after delivery of the user interface 150 and data elementscript 180 to the client 130 by the publisher server 110, thecomputer-executable instructions stored by the memory 206 furthercomprise instructions for the user interface 150, the data elementplayer 160, the video player 170, and the data element script 180 asshown in FIG. 2.

The pointing device 214 may be a mouse, track ball, touch screen, orother type of pointing device, and is used in combination with thekeyboard 210 to input data into the computer system 200. The graphicsadapter 212 displays images and other information on the display 218.The network adapter 216 couples the computer system 200 to the network190. Some embodiments of the computer 200 have different and/or othercomponents than those shown in FIG. 2.

The types of computers 200 used by the entities of FIG. 1 can varydepending upon the embodiment and the processing power required by theentity. For example, a client 130 that is a mobile telephone typicallyhas limited processing power, a small display 218, and might lack apointing device 214. A server providing a data element server 120, incontrast, might comprise multiple servers working together to providethe functionality described herein. Also, a server typically lackshardware such as the graphics adapter 212, the display 218, and userinput devices.

Some portions of the above description describe embodiments in terms ofalgorithms and symbolic representations of operations on information.For example, the description corresponding to FIGS. 2-12 relate totechniques that optimize data element usage. These algorithmicdescriptions and representations are commonly used by those skilled inthe data processing arts to convey the substance of their workeffectively to others skilled in the art. These operations, whiledescribed functionally, computationally, or logically, are understood tobe implemented by computer programs or equivalent electrical circuits,microcode, or the like. Furthermore, it has also proven convenient attimes, to depict to these arrangements of operations as modules, withoutloss of generality. The described operations and their associatedmodules may be embodied in software, firmware, hardware, or anycombinations thereof.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment. Software and firmware configurations of the modules andcorresponding instructions described above can be stored in, forexample, the storage device 208 and/or the memory 206 and can beexecuted by, for example, the processor 202, adapters 212, 216,controllers 220, 222, and/or multiple such devices operating inparallel.

FIG. 3 illustrates a block diagram of an object hierarchy according totechniques presented herein. In one technique, an owner 305, who may bea creator and/or producer of data elements, such as promotional dataelements, and may be a buyer of promotional space, may manage orotherwise own one or more campaigns 310. In general herein, a user ofthe application 140 and/or client 130 will be referred to as a “user,”though the user may or may not be the owner 305. A campaign 310 may be acollection of one or more data elements 315 that share a common idea ortheme. As discussed above, a data element 315 may comprise anycomputer-executable code whose execution may result in the presentationof text, images, and/or sounds to the user. Each data element 315 mayfurther comprise one or more creatives 320, each of which may correspondto at least a portion of the text, images, and/or sounds presented tothe user. Finally, each creative 320 may further comprise one or moremedia files 325, such as textual, image, and/or audio files.

In another technique, data elements may be organized into one or moredata element groups 330. The data element group 330 may enable users tomore effectively plan and optimize the meeting of constraints relatingto data elements that share common objectives and/or budgets. Forexample, a user may have a business objective of programming allocatingresources, such as a budget, across promotional data elements in themost cost-effective manner. The data element group 330 object may allowthe owner to organize groups of data elements 315 that share one or morecommon objectives. While one campaign may be associated with a pluralityof data element groups, the application may enforce a rule that dataelement groups cannot be shared across multiple campaigns. Further, theapplication may require that at least one data element 315 be associatedwith a data element group 330. While data elements 315 may be associatedwith a data element group 330, the application may allow data elements315 to remain unassociated with a data element group 330.

As will be shown, techniques discussed herein may allow an owner 305 tomonitor data element group 330 performance via one or more userinterfaces 150. Owners 305 may be able to forecast supply, pricing, andperformance associated with data elements 315 and data element groups330. Owners may also be able to programmatically optimize budgetallocation across data elements that perform the best according to oneor more objectives and/or one or more constraints. Owners may further beable to manually optimize allocations of resources, and may allocatemore resources, such as a budget, to better performing data elements.Owners may also be able to run reports against data element groupperformance, further enabling the selection of the most effective dataelements for reaching a given set of objectives and constraints.

FIG. 4 is a block diagram illustrating an example of the grouping ofdata elements into data element groups according to techniques presentedherein. As discussed above, data elements may be used as promotionalcontent, and may be associated with a brand 405. The medium for thepresentation of promotional content may be video 410, although othermediums would be consistent with techniques presented herein. A givenbrand 405 may have a one or more associated campaigns 310. Each campaign310 may, in turn, have any number of data element groups 330 and anynumber of data elements 315 associated therewith. Constraints may be setat the campaign level, for example, for resource constraints, such as abudget. These constraints may bind objects lower on the objecthierarchy, such as individual data element groups 330 and data elements315. Similarly, constraints set at the data element group level 330 maybind data elements 330 associated with the data element groups 330.

Data element groups 330 may be created by an owner 305, or some otheradministrator or sufficiently privileged user. Alternatively, dataelements may have associated tags and/or properties, and data elements315 may be automatically grouped into data element groups 330 based uponthese tags and/or properties. For example, data elements 315 sharingsimilar optimization objectives and/or maximum effective cost perthousand impressions (“eCPM”) may be grouped under one data elementgroup 330. Forecasts of delivery and/or key performance indicators(“KPIs”) may be determined and/or viewed at the data element group 330level as well at the level of individual data elements within the dataelement group. Budgets may be automatically allocated across multipledata elements 315 within a given data element group 330.

One or more users 415, which may correspond to one or more owners 305,may access the application in order to create and associate data elementgroups 330 and data elements 315. The data element groups 330 and dataelements 315 may utilize one or more creatives 420, which may be storedin a data store, such as storage device 208. Data element groups 330and/or data elements 315 may further be utilized in relation to one ormore sites 425, such as websites, and segments 430. Sites 425 may beused to target certain topics, for example sports and/or entertainment.Segments 430 may be target audience objects. For example, a data segment430 object may represent females 18 and over. Data element groups 330and/or data elements 315 may also be utilized in relation to geographicareas 435. For example, a data element group may contain onlypromotional data elements that are to be run in a particular geographicregion.

As discussed herein, objectives and/or constraints may be optimized inorder to meet or exceed the one or more objectives within anyconstraints. For example, objectives may be to hit a target eCPM for agiven budget, to maximize target impressions, to maximize the completionrate, and/or to maximize the click-through rate. Any number of otherobjectives or goals (“KPIs”) may be used. Optimization, for example of abudget, may occur in multiple ways. Optimization may occur across videochannels, for example by optimizing how much of a budget to apportion toeach video channel given an assessment of the effectiveness of thechannel towards meeting the one or more objectives. Optimization mayalso occur across multiple campaigns 310, a campaign being a collectionof related data elements 315 and/or data element groups 330. Onecampaign may be determined to be more effective than another campaign ina certain medium or in promoting a certain brand, and resources may bebalanced accordingly. Optimization may further occur across data elementgroups 330 within a given campaign. Some data element groups may proveto be more effective at meeting objectives and constraints than others,and resources may be continually rebalanced accordingly. Thisoptimization may include data elements that are not affiliated with dataelement groups. Optimization may further occur across data elements,such as the data elements within a given data element group. In thismanner, individual data elements that perform better for a given set ofobjectives and/or constraints may be given more resources or otherwiseutilized more frequently. Further, optimization may be applied at thelevel of creatives, which may comprise a portion or version of a givendata element. Optimization may also occur when selecting an optimalprice of a bid, if, for example, bidding for space to promote dataelements. Thus, optimization may occur at one or many levels of theobject hierarchies shown in FIG. 3.

FIG. 5 is an example campaign user interface 500 displaying dataelements and data element groups. The campaign user interface 500 maydisplay any campaigns 505 to which the application user has access toview, such as any and all campaigns associated with a given owner 305.The campaign user interface 500 may also display any data element groupsand/or independent data elements associated with each campaign. Eachcampaign 505 may have associated data element groups listed beneath,which may themselves have associated data elements listed beneath. Thedata element groups, data elements, and campaign listings may beexpanded by default, contracted by default, or some items may beexpanded or contracted, according to user preference. For example, auser may select that campaigns 505 are expanded, showing all associateddata element groups associated therewith, but the data element groupsthemselves may be contracted by default, such that the user cannotimmediately see the data elements associated with each data elementgroup.

The campaign user interface 500 may further display fields associatedwith each campaign, such as the campaign's name 510, active/inactivestatus 515, start and end dates 520, pacing 525, impressions 530 (numberof times a data element is presented to a member of the targetaudience), amount spend 535 thus far, bid 540 (which may comprise theaverage bid for impressions of a promotional data element, or perthousand impressions, etc.), click rate 545, targeting 550, and otheroptions 555.

Pacing 525 may indicate a rate at which data elements are being madeavailable relative to a goal. For example, in advertising, if a dataelement has a spend goal of $10 over a 10 second period, pacing is 100%if $10 is actually spent delivering the data element to an audienceduring that time. Pacing would be 200% if $20 was spent delivering thedata element during that time period, and so on.

Targeting may be a filtering mechanism to make sure that promotionaldata elements run against a qualified pool of inventory and audience.For example, certain devices (e.g., only tablet computers), platforms(e.g. only Android), topics (e.g., only sites that over index forsports), audience segments (e.g., only females 18 and up), time periods(e.g., only evenings), may be specifically targeted. Techniquesdiscussed herein may attempt to optimize for targeting to find the mostvaluable impressions for the cost.

A user of application 140 might create a new data element group in thecampaign user interface 500 in a number of ways. For example, the usermight select “create new” 510 to create a new data element group forassociation with a certain campaign. A user may also be able to clone anexisting data element group, which may clone the data element group andany associated data elements. A user may also be able to create ordelete data elements within a data element group, or data elements thatare unaffiliated with a data element group.

FIG. 6 is an example user interface 600 allowing the creation of one ormore data element groups according to techniques presented herein. Whena user selects to create a new data element group, user interface 600may be displayed, where a user may be able to select general settings,objectives, and/or constraints for the data element group. Theseselections may automatically propagate to any data elements associatedwith the data element group. Settings selected in the data element groupuser interface 600 may be reflected in the campaign user interface 500.

The user may also select a status 515 of the data element group, whichmay reflect whether any data elements in the data element group may beused in the real world, such as for advertising. The data element groupmay be “paused” by default, such that ad space associated with the dataelements of the data element group would not be obtained. If the dataelement group is set to “live,” a user may still be able to individuallypause data elements associated with the data element group.

The user may further select a data element group name 510, and maydesignate any notes or comments 605 about the data element group. Theuser may also select one or more start and end dates 607 of the dataelement group, which may represent dates promotional data elements mayrun in one or more forms of media. Additional start and end dates may bedesignated, such as by selecting a flight option 610. As used herein, aperiod between a start and end date may be known as a “flight.”

The user may also designate a data element group goal 615, which mayindicate a total budget (spend total) for the data element group. Theuser may also be able to switch auto-allocation 620 on and off, whichenables automatic optimization of data elements associated with the dataelement group, as will be further described herein. The objectives maybe optimized across data elements of the data element group based on“mid-flight” metrics or other performance data. Once a minimum level ofperformance data is obtained, usage of a given data element and budgetassociated therewith may be refined at regular time intervals, such asdaily. If the user turns off automatic allocation 620, the user maystill be able to manually allocate data element goals for data elements,for example in the data elements tab.

Goals or objectives set at the data element group level may be set toadhere to goals set at higher object levels, such as at the campaignlevel. For example, start and end date ranges 607 may be prohibited fromgoing outside of any start or end date ranges set at the campaign level.The spend total may be set to be equal to or less than the spend totalset at the campaign level, if set.

As an example, a user may set automatic budget allocation 620 across oneor more data element groups and independent data elements, and automaticperformance optimization for data element usage within a data elementgroup. Optimization techniques, such as linear programming, may be usedto optimize for multiple goals while staying within any number ofconstraints. The optimization may occur recursively up or down throughthe object hierarchy. A first constraint set at the campaign level maybe a spend goal of $2.5 million. The start and end dates may be set asthe month of October 2014. Based on one or more campaign-level or otherconstraints, data element groups may be automatically or manuallycreated corresponding to one or more audience segments.

The example campaign may contain any number of data element groups. Afirst data element group may correspond to would-be Toyota buyers. Auser may set delivery constraints at the data element group level. Forexample, the user may set the spend goal for the data element group tobe $1 million, and the maximum eCPM to be $15. The user may also setobjectives or goals, for example a target audience of females aged18-49, with a click-through rate goal of 2%.

A second data element group may correspond, for example, to Hondacustomers. A user may set delivery constraints at the data element grouplevel. For example, the user may set the spend goal for the data elementgroup to be $500,000, and the maximum eCPM to be $10. The user may alsoset objectives or goals, for example a target audience may be set toadults 18 and over, and a completion rate goal may be set to 85%. Asdiscussed above, individual data elements may be associated with acampaign regardless of whether they are associated with a data elementgroup. For example, the user may create an individual promotional dataelement with a spend goal of $500, the maximum eCPM of $12, and with anobjective of minimizing cost per thousand impressions (CPM). Further, asdiscussed above, while the campaign may be automatically optimized, auser may manually set budgets of unaffiliated data elements, one or moredata element groups and/or individual data elements within groups.

FIG. 7 is an example user interface 700 illustrating further usersettings that may be configured when creating a data element group. Theuser may designate other objectives and/or constraints in addition tobudget. For example, the user may designate impression targets or grossdata element revenue targets to be automatically optimized. As arestrictive goal, the user may also designate a cap for the frequency705 that a data element may be used in a campaign. Frequency capping maybe managed at the data element group level, and may override frequencycapping at the data element level. As noted previously, frequencycapping may conform to the frequency capping setting from the campaignobject.

Additional constraints may also be set by the user when creating acampaign, data element, or, in the example shown in FIG. 7, a dataelement group. The user may further set a cost per thousand impressionsprice cap 710. A buyer margin 715, for example a percentage of mediacosts and/or vendor fees, and a pass-through cost 720, such as a costper thousand constraint, may also be designated. Pass-through costs 720may include other costs and third party fees which may not be otherwisedirectly logged in application 140. Owners 305 may wish to includepass-through costs to ensure that the media cost numbers, which may bedefined as the maximum bid minus any pass-through costs, are realistic.For example, if the maximum bid is $10, but there are $3 in expecteddata costs, up to $7 is left to be allocated for the media cost. If anyof the billing fields are set, they may bind any data elementsassociated with the data element group. As a result, changing billingfields in the settings of associated data elements may be disabled.

FIG. 8 further illustrates the selection of objectives associated withthe creation of a data element group according to techniques presentedherein. Selections on the user interface 800 may be automaticallyapplied to one or more data elements associated with the data elementgroup. As discussed above, data element group objectives may beoptimized by allocating resources based upon performance of dataelements at some predetermined time period after the start of thecampaign (i.e. “mid-flight”). The performance of one or more associateddata elements may further be determined given any objectives and/orconstraints defined in the user interface 800.

The user may select whether optimization 805 for the data element groupis set to on, off, or manual. If the data elements are being used foradvertising, selecting “on” may mean that the application willautomatically deliver impressions based on a real-time marketplace foreach data element at the lowest possible price to meet the requiredobjectives of the data element group. Optimization 805 may be turned onby default. In order enable manual or turn off optimization 805 at thedata element group level, the user may be required to turn off “AutoAllocation Across Data Elements” in user interface 600. The optimization805 setting may automatically propagate to all data elements associatedwith the data element group.

The user may also select a target eCPM 810, which, as indicated by theasterisk, may be a mandatory field. As discussed above, the eCPM 810 isthe effective cost per thousand impressions, and may be calculated bydividing total earning for data elements in the data element group bytotal number of impressions of data elements in the data element groupin thousands. Associated data elements or “child data elements” may beunable to use a different target eCPM when data element groupoptimization 805 is turned on. The target eCPM 810 may be enforced as arestrictive ceiling. If automatic goal allocation 620 is turned off,child data elements may be able to have a different target eCPM as longas they are at or below the target eCPM goal. If automatic goalallocation 620 is turned on, the optimizer may determine the best way toallocate the budget (impression, spend or gross revenue) across anychild data elements. Once enabled, a user may be restricted from editingthe delivery goal, target eCPM, and/or objectives at the individual dataelement level.

Multiple objectives 815 may be selected by a user and ranked in theselections list 820. For example, a user may designate a primaryobjective, a secondary objective, a tertiary objective, etc. The dataelement group optimization algorithm may take the ranking into accountwhen optimizing allocation of resources across a given set of dataelements, given the assigned objectives and constraints. For example,objectives may be assigned varying weights that affect how optimizationis performed. Positive factoring may be given to higher priorityobjectives such that the application 130 may be more likely to bid forspace which meets a higher priority objective (within the maximum CPMgoal or other constraints), rather than a lower priority objective.

The importance of each objective in a list or hierarchy of objectivesmay be reflected by the allocated bidding price. The dedicated biddingprice allocated for each objective may be a weighted portion of themaximum CPM (or other budgetary constraint), while the weight of eachobjective may correspond to the priority level (i.e., higher priorityobjectives may be given higher weights). The assigned budget may also beadjusted by the achievement difficulty and rareness of a givenobjective.

For example, a user may designate a list of objectives 820. A primaryobjective may be in-target impressions of females 18 and up, a secondaryobjective may be a completion rate of at least 80%, and a tertiaryobjective may be a CTR of at least 1%. The total target eCPM 810 may be$15. Initially, a largest portion, for example 50%, of the eCPM may beautomatically allocated to the primary objective, in this case thein-target objective. A second largest portion, such as 33%, may beallocated to the secondary objective. And a smallest portion, such as17%, may be allocated to the tertiary objective. The bids may then beautomatically adjusted by rareness. For example, the average in-targetrate may be 25%, so in-target impressions may receive an eCPM of $30.Completed impressions may receive an eCPM of $6.25 (assuming an averagecompletion rate of 80%), and CTR may receive $250 eCPM (assuming anaverage CTR of 1%).

After the campaign begins, in mid-flight the allocations may be adjustedby achievement difficulty. For example, if the secondary objectiveaverage completion rate of 80% is achieved, allocations may be increasedto the primary and/or tertiary objectives to increase the likelihoodsthat they will also be met. In other words, allocations for objectiveswith a low achievement difficulty may be reduced relative to otherobjectives with a higher achievement difficulty. This behavior is notnecessarily binary. Rather, as a given objective becomes closer to beingmet, the allocation may be correspondingly reduced.

FIG. 9 is an example user interface 900 further allowing the creation,modification, and/or selection of data elements and attributes that maybe associated with the creation of a data element group. The userinterface 900 may list data elements associated with the data elementgroup 905. Each data element may have an associated status indicator910, a goal allocation 915, a start date and end date of use 920, forexample if the data elements are advertisements in a campaign, a pacingpercentage 925, which determines how promotional data elements arespaced out in time, a number of impressions 930, the spend total 935,the revenue generated 940, the associated bid 945, the click rate 950,the targeting 955, and other options 960 and 965. The user interface900, along with other user interfaces presented herein, may display aforecasted delivery, such as a supply curve for eCPM. The audience for aselected data element group or data element may also be forecasted 975.

Several of these fields will now be discussed in greater detail. Thegoal allocation field 915 may determine how much of an available budgetis intended to be spent on data elements in the data element group. Ifthe user has selected automatic allocation 620, the default may be setto auto in the goal allocation field 915. The user may be able to setthe goal allocation field 915 to manual, which may in turn switch theselection in the automatic allocation field 620 in a correspondingmanner. The percentages of the goal allocation field 915 may be requiredto total 100%. The allocation percentages may be not editable ifautomatic allocation 620 is enabled, as the percentages would be decidedby the optimization features of the application. If a budget has beenset at the data element group level, then the budgets of all of the dataelements within the data elements may be set to remain equal to or belowthe data element group total.

If, on the other hand, the goal allocation field 915 is set to manual, auser may be able to adjust the percentages of each data element. As adefault, a manual setting may result in the first data element receivinga 100% allocation, which may then be modified by the user. The goalestimate, which may be a dollar amount under the goal allocation field915, may be not editable by the user, and may be derived only from thepercentage designated and the budget of the data element group. The sumof the allocation percentages may be set to total 100%. A user may alsobe able to allow a portion of the budget to be automatically allocated,and a portion to be manually allocated.

Status indicators 910 may be placed near goal allocation percentages.The status indicators 910 may be colors or symbols, such as green orred, up or down arrows, etc. The status indicators 910 may have a firststatus if the allocation is increasing (an indication that the dataelement has been determined to be more effective at reaching objectivesthan in previous optimization cycles), and another status if theallocation is decreasing (an indication that the data element has beendetermined to be less effective at reaching objectives than in previousoptimization cycles). A third status may also exist if the allocationhas not changed in the recent allocation cycle from a prior allocationpercentage. For example, if a data element allocation is increasing, theassociated status indicator may be green. If the allocation isdecreasing, it may be red, and if the allocation is the same or within apredetermined distance from the same allocation, the status indicatormay be gray.

A user of the application may be able to add new data elements in one ormore ways, such as by clicking a button 965 on the user interface. Auser may further be able to edit the settings of each data element,clone a data element (which may be automatically associated with thedata element group), and/or delete data elements.

One or more user interfaces presented herein may also display a forecastwindow 968. A user may be able to toggle between different forecastviews. A default view may be an aggregated data element group forecast.An aggregated data element group forecast is a summarized view of allchild data elements. Alternatively or in addition, the application mayalso be able to display a forecast window for individual data elements.In response to a user clicking on an individual data element, theforecast window for the individual data element may be displayed.Similarly, clicking on a data element group may cause the aggregateddata element group forecast to be displayed.

The application may also create reports about data element groups anddata elements for display. Report keys may comprise one or more of: adata element group, data element group end date, data element group goalor objective, data element goal or objective type, data element groupidentifier, data element group pricing type, data element group startdate, and a data element group type. Similar reports may be generatedfor individual data elements, for campaigns, or for a group ofassociated campaigns.

Data element group optimization will now be further discussed. At a highlevel, data element group optimization may involve a user inputting astrategy. Accordingly, objectives and/or constraints such as flight,budget, performance goals and associated data elements may be defined.The data elements may have a common KPI goal, which will guide theapplication to allocate the budget accordingly. Once the data elementgroup has been created, promotional data elements may be run, andassociated performance data may be collected, for example on a certaintime interval such as on a daily basis. The budget may then be assignedacross data elements based on the performance data and any constraintsor restrictions (e.g. a minimum spend notwithstanding goal). Theperformance data may allow a determination of which data elements arebetter meeting the one or more objectives, such as, for example, byattracting higher click rates and/or click-through rates. The budget maybe assigned for a subsequent time interval, such as for a day, and itmay be reevaluated at each time interval. Alternatively, optimizationmay be performed in real time. Each data element may be given a trainingbudget such that each data element may have the chance to prove itselfeffective, for example in an advertising scenario. A statisticallysignificant amount of performance data for a given data element may berequired to be collected before a data element's budget may be reducedbelow a predetermined threshold, such as being reduced to zero. Dataelement groups may be required to make a first budget allocation to eachdata element in the group within a predetermined time period, such aswithin 48 hours of the data element group flight. This process mayiteratively loop each time interval until the campaign ends. Over time,as performance data accumulates, the application may become moreaggressive in assigning resources such as budget to data elements thatprove to be more effective.

For data elements with in-target goals, where there is no feedback loop,it may be assumed that if a data element is using an online campaignratings (OCR) application, a feedback loop may be used to assess actualperformance. If a data element is not using an OCR application, it maybe assumed that the optimization estimates were delivered.

Possible features of the data element group optimization algorithm willnow be discussed in greater detail. The algorithm may determine theselected goals of the data element group, for example budget desired,cost per thousand cap, KPI goals, flight length, etc. After the dataelement group has run for a period of time, such as a predetermined timeinterval, the achieved goals of the data element group may bedetermined, such as budget delivered, money spent, KPIs delivered, etc.The data element group plan for the next time interval, such as apredetermined time interval cycle, may then be determined. Based on thebudget delivered, the minimal training size of the data element groupmay be determined. Based on the average KPI (supply), the KPI goals(demand), and the indicated priorities, the value of each KPI may beevaluated (the same as controller optimization) as a data element groupbuying plan. The achieved KPI of each data element in the data elementgroup may then be read, for example, in terms of click counts,completion counts, conversion counts, etc. Achieved KPI performance datamay be converted to click rate, completion rate and conversion rate,etc. Based on the achieved KPI of each data element and the determinedvalue of each KPI, the value of each data element towards the dataelement group may be evaluated. The value may be determined as, forexample, by multiplying the KPI of the data element by the buying plan(the buying plan may be a representation of the importance of eachobjective) of the data element group. The forecasted KPI of each dataelement in the next cycle may then be read. Based on a received ordetermined forecasted KPI, the opportunity risk of the value of the dataelement dropping may be modeled. The forecasted supply of each dataelement in the next cycle may then be received or determined. The budgetfor each data element in the data element group may then be optimized,for example by linear programming. A feasibility region within a rangeof constraints may be determined, and the optimum distribution ofresources may further be determined in part based upon which point inthe feasibility region most effectively meets the objectives. Inparticular, intersection points of constraint lines along edges of thefeasibility region may be evaluated. The optimization may be determined,for example, using the following technique:

MAX sum(DEperform*DEbudget)

S.T sum(DEbudget)=DEGgoal

0<=sum(DEbudget*eCPM)<=DEGspend_goal

BOUNDS: DEmingoal<=DEbudget<=DEmaxsupply

The objective of these equations may be to maximize the aggregatedperformance at the data element group level. The DEbudget may representthe budget allocated to each data element in a data element group, whileDEperform may represent the performance of each data element. Theconstraints may be data element group level budget (DEGgoal andDEGspend_goal), and data element level supply (DEmaxsupply) and a dataelement configuration goal minimum (DEmingoal). The solution may then beprovided to the data element level optimizer.

FIG. 10 is an example user interface 1000 configured for enablingcreating and/or editing data elements that may be associated with a dataelement group. After selecting to create a data element in theapplication 140, such as one associated with a data element group, theuser may be shown user interface 1000. The status 1005 of the dataelement may be shown as either paused or live. If a data element ischanged from live to paused mid-flight (during a campaign), a warningmessage may be displayed, and subsequent action by the applicationtaken, that automatic allocation will be set to 0% in the data elementgroup user interface 900 and elsewhere. The budget may then bere-optimized for any remaining live data elements in the data elementgroup, and/or for any data elements left in the campaign generally. Auser may be prohibited from changing the status from live to pausedunless he or she has also set the manual goal allocation to 0% in thedata element group user interface 900.

The data element name 1010 may be designated by the user. The dataelement group 1015 associated with the data element may also beindicated. As discussed previously, the data element may inherit pricingand optimization criteria, as well as any other restrictions orconstraints set at the data element group level.

A start date and end date 1020 (flight), in the case of published orpromotional data elements, may also be displayed and input by the user.Flight dates of individual data elements may be prohibited from fallingoutside of the one or more flight dates of the parent data elementgroup.

Objectives or goals 1025 may also be designated at the data elementlevel. However, if the data element group optimization is activated,this section may be deactivated. A message may be provided to the userindicating that, to enable data element goal selection, the autoallocation across data elements 620 should be turned off. The dataelement group optimization algorithm may automatically allocate a subsetof the data element group.

If the automatic allocation across data elements 620 is set to on, theremay be no specific goals that can be added for the data element.However, the user may be able to determine a minimum level allocationper data element. For example, the user may set the minimum number ofimpressions that must be provided for the data element, in the case ofadvertising. The minimum level (along with the combined minimum levelsset by other data elements) may be prohibited from exceeding the valueset at the data element group level. The minimum goal may be allocatedto the data element even if the data element is underperforming relativeto other data elements on the selected optimization objectives. Even ifa minimum goal is selected, the application optimizer may stilldesignate a higher number if the data element outperforms other dataelements in the data element group based on, for example, mid-flightperformance.

FIG. 11 is an example user interface 1100 displaying a data element bidand optimization page, which may be associated with the creation and/orediting of data element metadata, according to techniques presentedherein. If the associated data element is part of a data element groupwith optimization 1105 turned on, the user interface 1100 may benon-editable. The user may not be able to change the target eCPM 1110 orany of the objectives 1115. If, however, automatic goal allocation isset to manual, or turned off, the user may be able to edit the targeteCPM and/or objectives 1115. As a result, each data element within adata element group and campaign may be able to have differentoptimization goals.

As discussed above, optimization may be performed across data elementgroups 330, as well as across particular data elements 315, regardlessof whether they are affiliated with a data element group. The objectivesand/or constraints of the data element may be determined, for example,in terms of budget desired, cost per thousand impressions cap, KPIgoals, flight length, etc. The achieved goals and/or constraints of thedata element may also be determined, for example, in terms of budgetdelivered, money spent, KPI delivered, etc. The data element budget,cost per thousand impressions cap, etc., may then be determined for thenext cycle, where the cycle may be a predetermined period of time. Basedon the pacing of the budget goal, e.g., the rate at which the budget isbeing spent, the base value may be adjusted. Adjusting the base valuemay help keep pacing at or near 100%. The base value may represent theimportance of the pacing objective. Pacing may automatically be given anon-configurable top priority, so the system may calculate a base valuefirst. The importance of each KPI may then be determined based on theaverage KPI (supply, the KPI goal (demand), and/or any user-indicatedpriorities. The optimization algorithm may then attempt to maximize theone or more KPIs with respect to cost per thousand impressions.

FIG. 12 is an example block diagram illustrating systems and methodsaccording to techniques presented herein. At step 1205, a plurality ofuser-defined objectives associated with a group of data elements may bereceived. At step 1210, one or more constraints associated with thegroup of data elements may be received, wherein at least one of theconstraints comprises resources apportionable to each data element inthe group of data elements. At step 1215, at least a portion of theresources may be apportioned to each data element in the group of dataelements in a manner which meets the one or more constraints. At step1220, one or more metrics associated with the performance of the groupof data elements in meeting the plurality of user-defined objectives maybe received. At step 1225, an effectiveness may be determined for eachdata element in the group of data elements for meeting the plurality ofuser-defined objectives, wherein the effectiveness is determined basedon the one or more metrics. At step 1230, the at least a portion ofresources associated with each data element in the group of dataelements may be automatically revised based on the determinedeffectiveness in a manner optimized to meet the plurality ofuser-defined objectives within the one or more constraints.

Techniques presented herein may provide a differentiated buying toolallowing one or more owners 305 to purchase space for promotional dataelements 315 that may eliminate substantial manual work and providereal-time optimal allocation of resources to data elements. In addition,the optimization algorithm may work recursively up or down the objecthierarchy, thus increasing the efficiency of optimization according toobjectives and/or constraints. While many settings discussed herein maybe able to be set at the data element group level, these same settingsmay be set at the campaign level and/or data element level, unlessexpressly stated otherwise herein. More generally, any setting which maybe configured at any level of the object hierarchy may also beconfigured at any other level of the object hierarchy, unless expresslystated otherwise herein. All user interfaces shown herein, orcombinations thereof, may be present in various embodiments, and may bepresented to one or more users. All features discussed herein may haveassociated security requirements before they may be used. For example,different users of the application may have different levels ofprivileges, allowing them to access differing features of theapplication. In addition, many steps of techniques discussed herein aredisclosed in a particular order. In general, steps discussed may beperformed in any order, unless expressly stated otherwise.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. It should be understood thatthese terms are not intended as synonyms for each other. For example,some embodiments may be described using the term “connected” to indicatethat two or more elements are in direct physical or electrical contactwith each other. In another example, some embodiments may be describedusing the term “coupled” to indicate that two or more elements are indirect physical or electrical contact. The term “coupled,” however, mayalso mean that two or more elements are not in direct contact with eachother, but yet still co-operate or interact with each other. Theembodiments are not limited in this context.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the invention. Thisdescription should be read to include one or at least one and thesingular also includes the plural unless it is obvious that it is meantotherwise.

Upon reading this disclosure, those of skill in the art will appreciatestill additional alternative structural and functional designs forsystems and a methods for optimizing data element usage through thedisclosed principles herein. Thus, while particular embodiments andapplications have been illustrated and described, it is to be understoodthat the disclosed embodiments are not limited to the preciseconstruction and components disclosed herein. Various modifications,changes and variations, which will be apparent to those skilled in theart, may be made in the arrangement, operation and details of the methodand apparatus disclosed herein without departing from the spirit andscope defined in the appended claims.

1-20. (canceled)
 21. A computer-implemented method for optimizinggraphical data element usage according to the plurality of objectives,comprising: receiving, at a server, a plurality of objectives associatedwith one or more graphical data elements via a user interface;receiving, at the server, one or more fiscal constraints associated withthe graphical data elements via the user interface; apportioning, by theserver, at least a portion of fiscal distribution resources to eachgraphical data element of the graphical data elements within the one ormore fiscal constraints; receiving, at the server, one or moreelectronic distribution metrics associated with the performance of thegraphical data elements in meeting the plurality of objectives, whereinthe one or more electronic distribution metrics are associated withdistribution across an electronic network; automatically revising, atthe server, the at least a portion of fiscal distribution resourcesassociated with each graphical data element of the graphical dataelements in a manner optimized to meet the plurality of objectiveswithin the one or more fiscal constraints by determining one or morecandidate optimization points based on the one or more fiscalconstraints and the plurality of objectives; and distributing, by theserver, the automatically revised portion of fiscal distributionresources associated with each graphical element.
 22. Thecomputer-implemented method of claim 21, wherein the automatic revisingcomprises: weighting each objective of the plurality of objectives, andrevising the at least a portion of fiscal distribution resourcesassociated with each graphical data element in the graphical dataelements based on the determined effectiveness in a manner prioritizinga highest-weighted objective of the plurality of objectives.
 23. Thecomputer-implemented method of claim 21, wherein at least one of thefiscal constraints comprises fiscal distribution resources apportionablefor the distribution of each graphical data element in the graphicaldata elements.
 24. The computer-implemented method of claim 21, furthercomprising: determining, at the server, an effectiveness of eachgraphical data element in the graphical data elements for meeting theplurality of objectives, wherein the effectiveness is determined basedon the one or more electronic distribution metrics.
 25. Thecomputer-implemented method of claim 21, further comprising: receivingan additional fiscal constraint for association with the one or morefiscal constraints; and automatically revising the at least a portion offiscal distribution resources associated with each graphical dataelement in the graphical data elements based on the additional fiscalconstraint.
 26. The computer-implemented method of claim 21, wherein theoptimization server comprises a plurality of servers.
 27. Thecomputer-implemented method of claim 21, further comprising: designatinga predetermined minimum allocation of the fiscal distribution resourcesto each graphical data element in the graphical data elements until apredetermined minimum level of electronic distribution metricsassociated with each graphical data element in the graphical dataelements is obtained.
 28. The computer-implemented method of claim 27,further comprising: lowering the predetermined minimum allocation of thefiscal distribution resources for each graphical data element in thegraphical data elements as electronic distribution metrics are obtainedassociated with each graphical data element in the graphical dataelements.
 29. The computer-implemented method of claim 21, whereinautomatically revising the at least a portion of fiscal distributionresources associated with each graphical data element in the graphicaldata elements further comprises: determining intersection points ofconstraints to form the one or more candidate optimization points; andselecting a candidate optimization point from the one or more candidateoptimization points by determining which candidate optimization pointmaximizes a highest priority user-defined objective from the pluralityof user-defined objectives.
 30. The computer-implemented method of claim21, wherein the one or more electronic distribution metrics aredetermined over a predetermined time interval.
 31. Thecomputer-implemented method of claim 21, wherein each graphical dataelement in the graphical data elements corresponds to a promotionalvideo.
 32. A system for optimizing graphical data element usageaccording to objectives, the system including: a data storage devicestoring instructions for optimizing graphical data element usageaccording to objectives; and a processor configured to execute theinstructions to perform a method including: receiving a plurality ofobjectives associated with one or more graphical data elements via auser interface; receiving one or more fiscal constraints associated withthe graphical data elements via the user interface; apportioning atleast a portion of fiscal distribution resources to each graphical dataelement of the graphical data elements within the one or more fiscalconstraints; receiving one or more electronic distribution metricsassociated with the performance of the graphical data elements inmeeting the plurality of objectives, wherein the one or more electronicdistribution metrics are associated with distribution across anelectronic network; automatically revising the at least a portion offiscal distribution resources associated with each graphical dataelement of the graphical data elements in a manner optimized to meet theplurality of objectives within the one or more fiscal constraints bydetermining one or more candidate optimization points based on the oneor more fiscal constraints and the plurality of objectives; anddistributing, by the server, the automatically revised portion of fiscaldistribution resources associated with each graphical element.
 33. Thesystem of claim 32, wherein the processor is further configured for:receiving an additional fiscal constraint for association with the oneor more fiscal constraints; and automatically revising the at least aportion of fiscal distribution resources associated with each graphicaldata element in the graphical data elements based on the additionalfiscal constraint.
 34. The system of claim 32, wherein the optimizationserver comprises a plurality of servers.
 35. The system of claim 32,wherein the processor is further configured for: designating apredetermined minimum allocation of the fiscal distribution resources toeach graphical data element in the graphical data elements until apredetermined minimum level of electronic distribution metricsassociated with each graphical data element in the graphical dataelements is obtained.
 36. The system of claim 35, wherein the processoris further configured for: lowering the predetermined minimum allocationof the fiscal distribution resources for each graphical data element inthe graphical data elements as electronic distribution metrics areobtained associated with each graphical data element in the graphicaldata elements.
 37. The system of claim 32, wherein automaticallyrevising the at least a portion of fiscal distribution resourcesassociated with each graphical data element in the graphical dataelements further comprises: determining intersection points ofconstraints to form the one or more candidate optimization points; andselecting a candidate optimization point from the one or more candidateoptimization points by determining which candidate optimization pointmaximizes a highest priority user-defined objective from the pluralityof user-defined objectives.
 38. A non-transitory computer-readablemedium storing instructions that, when executed by at least oneprocessor, cause the at least one processor to perform a method ofoptimizing graphical data element usage according to objectives, themethod including: receiving, at a server, a plurality of objectivesassociated with one or more graphical data elements via a userinterface; receiving, at the server, one or more fiscal constraintsassociated with the graphical data elements via the user interface;apportioning, by the server, at least a portion of fiscal distributionresources to each graphical data element of the graphical data elementswithin the one or more fiscal constraints; receiving, at the server, oneor more electronic distribution metrics associated with the performanceof the graphical data elements in meeting the plurality of objectives,wherein the one or more electronic distribution metrics are associatedwith distribution across an electronic network; automatically revising,at the server, the at least a portion of fiscal distribution resourcesassociated with each graphical data element of the graphical dataelements in a manner optimized to meet the plurality of objectiveswithin the one or more fiscal constraints by determining one or morecandidate optimization points based on the one or more fiscalconstraints and the plurality of objectives; and distributing, by theserver, the automatically revised portion of fiscal distributionresources associated with each graphical element.
 39. The non-transitorycomputer-readable medium of claim 38, wherein the processor is furtherconfigured for: designating a predetermined minimum allocation of thefiscal distribution resources to each graphical data element in thegraphical data elements until a predetermined minimum level ofelectronic distribution metrics associated with each graphical dataelement in the graphical data elements is obtained.
 40. Thenon-transitory computer-readable medium of claim 38, whereinautomatically revising the at least a portion of fiscal distributionresources associated with each graphical data element in the graphicaldata elements further comprises: determining intersection points ofconstraints to form the one or more candidate optimization points; andselecting a candidate optimization point from the one or more candidateoptimization points by determining which candidate optimization pointmaximizes a highest priority user-defined objective from the pluralityof user-defined objectives.